REVIEW
Harnessing the future: cutting-edge technologies for plant disease control
1
Faculty of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali, Bangladesh
2
College of Agriculture and Life Sciences, Kyungpook National University, Daegu, Republic of Korea
3
Department of Plant Pathology, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh
4
Ministry of Food and Agriculture, Plant Protection and Regulatory Services Directorate, Ashanti, Ghana
5
Plant Disease Clinic and Bank of Pathogens, Institute of Plant Protection – National Research Institute, Poznan, Poland
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2023-06-29
Acceptance date: 2023-09-04
Online publication date: 2023-12-05
Corresponding author
Kallol Das
College of Agriculture and Life Sciences, Kyungpook National University, Daegu, Republic of Korea
College of Agriculture and Life Sciences, Kyungpook National University, Daegu, Republic of Korea
Journal of Plant Protection Research 2023;63(4):387-398
HIGHLIGHTS
- Discover groundbreaking technologies that are reshaping the landscape of plant disease control.
- Explore cutting-edge tools and techniques that enable targeted interventions to protect crops from devastating diseases.
- Uncover the latest advancements in genetics, sensing, and AI, driving a paradigm shift in plant disease management.
KEYWORDS
TOPICS
ABSTRACT
The world population is projected to reach 9.8 billion in 2050, and 11.2 billion in 2100
(United Nations) and people will need food, and decrease the farming land. Thus, the importance
of Internet of Things (IoT) and Computer Science (CS) in plant disease management
are increasing now-a-days. Mobile apps, remote sensing, spectral signature analysis,
artificial neural networks (ANN), and deep learning monitors are commonly used in plant
disease and pest management. IoT improves crop yield by fostering new farming methods
along with the improvement of monitoring and management through cloud computing. In
the quest for effective plant disease control, the future lies in cutting-edge technologies. The
integration of IoT, artificial intelligence, and data analytics revolutionizes monitoring and
diagnosis, enabling timely and precise interventions. Cloud computing facilitates real-time
data sharing and analysis empower farmers to combat diseases with unprecedented efficiency.
By harnessing these innovations, agriculture can embrace sustainable practices and
safeguard crop health, ensuring a bountiful and secure future for the global food supply.
ACKNOWLEDGEMENTS
We would like to express our gratitude to all the co-authors
for their contribution and critical reviews from
the anonymous reviewers.
RESPONSIBLE EDITOR
Lidia Irzykowska
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (79)
1.
Abdullah H., Skidmore A.K., Darvishzadeh R., Heurich M. 2019. Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus L.) attack. International Journal of Applied Earth Observation and Geoinformation 82: 101900. DOI: https://doi.org/10.1016/j.jag.....
2.
Akbar S. 2020. Handbook of 200 Medicinal Plants: A Comprehensive Review of Their Traditional Medical Uses and Scientific Justifications. Springer: Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-....
3.
Alam F., Mehmood R., Katib I., Albogami N.N., Albeshri A. 2027. Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access. 5: 9533–9554. DOI: https://doi.org/10.1109/ACCESS....
4.
Aqeel-Ur-Rehman, Abbasi A.Z., Islam N., Shaikh Z.A. 2014. A review of wireless sensors and networks’ applications in agriculture. Computer Standards & Interfaces 36 (2): 263–270. DOI: https://doi.org/10.1016/j.csi.....
5.
Ardagna D., Cappiello C., Samá W., Vitali M. 2018. Context-aware data quality assessment for big data. Future Generation Computer Systems 89: 548–562. DOI: https://doi.org/10.1016/j.futu....
6.
Asghari P., Rahmani A.M., Seyyed Javadi H.H. 2019. Internet of things applications: a systematic review. Computer Networks 148: 241–261. DOI: https://doi.org/10.1016/j.comn....
7.
Aubert B.A., Schroeder A., Grimaudo J. 2012. IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support System 54 (1): 510–520. DOI: https://doi.org/10.1016/j.dss.....
8.
Bochtis D.D., Sørensen C.G.C., Busato P. 2014. Advances in agricultural machinery management: a review. Biosystems Engineering 126: 69–81. DOI: https://doi.org/10.1016/j.bios....
9.
Buja I., Sabella E., Monteduro A.G., Chiriacò M.S., de Bellis L., Luvisi A., Maruccio G. 2021. Advances in plant disease detection and monitoring: from traditional assays to in-field diagnostics. Sensors 21 (6): 2129. DOI: https://doi.org/10.3390/s21062....
10.
Carlos M.O.J., María H.C.L., Veronica B.F. 2019. Detection of significant wavelengths for identifying and classifying Fusarium oxysporum during the incubation period and water stress in Solanum lycopersicum plants using reflectance spectroscopy. Journal of Plant Protection Research 59: 244. DOI: https://doi.org/10.24425/jppr.....
11.
Castro A.I.D., Ehsani R., Ploetz R., Crane J.H., Abdulridha J. 2015. Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sensing of Environment 171: 33–44. DOI: https://doi.org/10.1016/j.rse.....
12.
Chen C.J., Huang Y.Y., Li Y.S., Chang C.Y., Huang Y.M. 2020. An AIoT based smart agricultural system for pests detection. IEEE Access 8: 180750–180761. DOI: http://dx.doi.org/10.1109/ACCE....
13.
Chouhan S.S., Uday., Singh P., Jain S. 2020. Applications of computer vision in plant pathology: a survey. Archives of Computational Methods in Engineering 27: 611–632. DOI: https://doi.org/10.1007/s11831....
14.
Cressman K. 1998. Monitoring locusts in the Middle East: an overview. Yale F&ES Bulletin 103: 123–140.
15.
Das K., Ayim B.Y., Borodynko-Filas N., Das S.C., Aminuzzaman F. 2023. Genome editing (CRISPR/Cas9) in plant disease management: challenges and future prospects. Journal of Plant Protection Research 63 (2): 159-172. DOI: https://doi.org/10.24425/jppr.....
16.
Ding W.G., Taylor G. 2016. Automatic moth detection from trap images for pest management. Computers and Electronics in Agriculture 123: 17. DOI: https://doi.org/10.1016/j.comp....
17.
Eberlein J., Davenport B., Nguyen T.T., Victorino F., Jhun K., van der Heide V., Kuleshov M., Ma’ayan A., Kedl R., Homann D. 2020. Chemokine signatures of pathogen-specific T cells I: effector T cells. Journal of Immunology 205: 2169–2187. DOI: https://doi.org/10.4049/jimmun....
18.
Elijah O., Rahman T.A., Orikumhi I., Leow C.Y., Nour Hindia M.H.D. 2018. An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things Journal 5 (5): 3758–3773. DOI: https://doi.org/10.1109/JIOT.2....
19.
Eliopoulos P.A., Potamitis I., Kontodimas D.C., Givropoulou E.G. 2015. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. Journal of Economic Entomology 108: 2808. DOI: https://doi.org/10.1093/jee/to....
20.
Fantin E., Raj I., Appadurai M., Athiappan K. 2021. Precision farming in modern agriculture. p. 61–88. In: “Smart Agriculture Automation Using Advanced Technologies” (Choudhury A., Biswas A., Singh T.P., Ghosh S.K., eds.). Springer, Singapore. DOI: http://dx.doi.org/10.1007/978-....
21.
FAO. 2017. Food and Agriculture Organization of the United Nations Plant Health and Food Security. International Plant Protection Convention, Rome, Italy.
22.
FAO. 2021. Food and Agriculture Organization of the United Nations Plant Health and Food Security. International Plant Protection Convention, Rome, Italy.
23.
Farooq M.S., Riaz S., Abid A., Abid K., Naeem M.A. 2019. A survey on the role of IoT in agriculture for the implementation of smart farming. IEEE Access 7: 156237–156271. DOI: https://doi.org/10.1109/ACCESS....
24.
Ferentinos K.P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311–318. DOI: https://doi.org/10.1016/j.comp....
25.
Furlanetto R.H., Nanni M.R., Mizuno M.S., Crusiol L.G.T., da Silva C.R. 2021. Identification and classification of Asian soybean rust using leaf-based hyperspectral reflectance. International Journal of Remote Sensing 42: 4177–4198. DOI: https://doi.org/10.1080/014311....
26.
Ghosh S., Dasgupta R. 2022. Machine learning in plant disease research. In: “Machine Learning in Biological Sciences”. Springer, Singapore. DOI: https://doi.org/10.1007/978-98....
27.
Golob A., Kavˇciˇc J., Stibilj V., Gaberšˇcik A., Vogel-Mikuš K., Germ M. 2017. The effect of selenium and UV radiation on leaf traits and biomass production in Triticum aestivum L. Ecotoxicology and Environmental Safety 136: 142–149. DOI: https://doi.org/10.1016/j.ecoe....
28.
Gomes J.F.S., Leta F.R. 2012. Applications of computer vision techniques in the agriculture and food industry: a review. European Food Research and Technology 235 (6): 989–1000. DOI: https://doi.org/10.1007/s00217....
29.
Guo Y., Chen S., Wu Z., Wang S., Robin B.C., Senthilnath J., Cunha M., Fu Y.H. 2021. Integrating spectral and textural information for monitoring the growth of pear trees using optical images from the UAV platform. Remote Sensing 13: 1795. DOI: https://doi.org/10.3390/rs1309....
30.
Hahn F. 2009. Actual pathogen detection: sensors and algorithms – a review. Algorithms 2: 301–338. DOI: https://doi.org/10.3390/a20103....
31.
Holguin G.A., Lehman B.L., Hull L.A., Jones V.P., Park J. 2010. Electronic traps for automated monitoring of insect populations. IFAC Proceedings Volumes 43 (26): 49–54. DOI: https://doi.org/10.3182/201012....
32.
Huang W., Lamb D.W., Niu Z., Zhang Y., Liu L., Wang J. 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8: 187–197. DOI: https://doi.org/10.1007/s11119....
33.
Hunter M., Smith R., Schipanski M., Atwood L., Mortensen D. 2017. Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67 (4): 386–391. DOI: https://doi.org/10.1093/biosci....
34.
Immitzer M., Atzberger C. 2014. Early detection of bark beetle infestation in Norway spruce (Picea abies L.) using WorldView-2. Photogramm. Fernerkund 5: 351–67. DOI: http://dx.doi.org/10.1127/1432....
35.
Ji R., Xie B.Y., Li D.M., Li Z., Zhang X. 2004. Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems & Environment 104: 615–620. DOI: https://doi.org/10.1016/j.agee....
36.
Kuchumov R., Sokolov A., Korkhov V. 2019. Staccato: shared-memory work-stealing task scheduler with cache-aware memory management. International Journal of Web and Grid Services 15 (4): 394– 407. DOI: https://doi.org/10.1504/ijwgs.....
37.
Kumar P.M., Hong C.S., Afghah F., Manogaran G., Yu K., Hua Q., Gao J. 2021. Clouds proportionate medical data stream analytics for internet of things-based healthcare systems. IEEE Journal of Biomedical and Health Informatics 26: 973–982. DOI: https://doi.org/10.1109/JBHI.2....
38.
Kuska M.T., Mahlein A.-K. 2018. Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. European Journal of Plant Pathology 152 (4): 987–992. DOI: https://doi.org/10.1007/s10658....
39.
Latchininsky A.V. 2013. Locusts and remote sensing: a review, special section on advances in remote sensing applications for locust habitat monitoring and management. Journal of Applied Remote Sensing 7 (1): 075099. DOI: https://doi.org/10.1117/1.JRS.....
40.
Lee H., Moon A., Moon K., Lee Y. 2017. Disease and pest prediction IoT system in orchard: a preliminary study. p. 525–527. In: Proceedings of the 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). Milan, Italy. DOI: http://dx.doi.org/10.1109/ICUF....
41.
Leeuw D.J., Vrieling A., Shee A., Atzberger C., Hadgu K.M., Biradar C.M., Keah H., Turvey C. 2014. the potential and uptake of remote sensing in insurance: a review. Remote Sensing 6: 10888–10912. DOI: https://doi.org/10.3390/rs6111....
42.
Li H., Zhao C., Yang G., Feng H. 2015. Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes. Remote Sensing of Environment 169: 358–374. DOI: https://doi.org/10.1016/j.rse.....
43.
Li L., Zhang S., Wang B. 2021. Plant disease detection and classification by deep learning – a review. IEEE Access 9: 56683–56698. DOI: https://doi.org/10.1109/ACCESS....
44.
Liakos K.G., Busato P., Moshou D., Pearson S., Bochtis D. 2018. Machine learning in agriculture: a review. Sensors 18 (8): 1–29. DOI: https://doi.org/10.3390/s18082....
45.
Liang Q., Xiang S., Hu Y., Coppola G., Zhang D., Sun W. 2019. PD 2 SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Computers and Electronics in Agriculture 157: 518–529. DOI: http://dx.doi.org/10.1016/j.co....
46.
Ma H., Pang X. 2019. Research and analysis of sport medical data processing algorithms based on deep learning and internet of things. IEEE Access 7: 118839–118849. DOI: https://doi.org/10.1109/ACCESS....
47.
Maduranga M.W.P., Abeysekera R. 2020. Machine learning applications in IoT based agriculture and smart farming: a review. International Journal of Engineering Applied Sciences and Technology 4 (12): 24–27. DOI: http://dx.doi.org/10.33564/IJE....
48.
Mahlein A., Rumpf T., Welke P., Dehne H., Plümer L., Steiner U., Oerke E. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128: 21–30. DOI: https://doi.org/10.1016/j.rse.....
49.
Mahlein A.K., Kuska M., Behmann J., Polder G., Walter A. 2018. Hyper-spectral sensors and imaging technologies in phytopathology: state of the art. Annual Review of Phytopathology 56 (1): 535–558. DOI: https://doi.org/10.1146/annure....
50.
Majid K., Herdiyeni Y., Rauf A. 2013. I-PEDIA: Mobile application for paddy disease identification using fuzzy entropy andprobabilistic neural network. p. 403–406. In: Proceedings of the 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). Bali, Indonesia. DOI: https://doi.org/10.1109/ICACSI....
51.
Mandi S.S. 2016. Natural UV Radiation in Enhancing Survival Value and Quality of Plants. Springer, New Delhi, India. DOI: http://dx.doi.org/10.1007/978-....
52.
McBratney A., Whelan B., Ancev T. 2005. Future directions of precision agriculture. Precision Agriculture 6: 7–23. DOI: https://doi.org/10.1007/s11119....
53.
Mendes J., Pinho T.M., dos Santos F.N., Sousa J.J., Peres E., Boaventura-Cunha J., Cunha M., Morais R. 2020. Smartphone applications targeting precision agriculture practices – a systematic review. Agronomy 10: 855. DOI: https://doi.org/10.3390/agrono....
54.
Mohanty S.P., Hughes D.P., Salathé M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1419. DOI: https://doi.org/10.3389/fpls.2....
55.
Moshou D., Bravo C., Oberti R., West J.S., Ramon H., Vougioukas S., Bochtis D. 2011. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering 108: 311–321. DOI: https://doi.org/10.1016/j.bios....
56.
Mrisho L.M., Mbilinyi N.A., Ndalahwa M., Ramcharan A.M., Kehs A.K., McCloskey P.C., Murithi H., Hughes D.P., Legg J.P. 2020. Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava – CMD and CBSD. Frontiers in Plant Science 11: 590889. DOI: https://doi.org/10.3389/fpls.2....
57.
Mullen K.E. 2016. Early detection of mountain pine beetle damage in Ponderosa pine forests of the Black Hills using hyperspectral and WorldView-2 data. MA Thesis, Minn. State Univ., Mankato.
58.
Ngie A., Abutaleb K., Ahmed F., Darwish A., Ahmed M. 2014. Assessment of urban heat island using satellite remotely sensed imagery: a review. South African Geographical Journal 96 (2): 198–214. DOI: https://doi.org/10.1080/037362....
59.
Ouhami M., Hafiane A., Es-Saady Y., El Hajji M., Canals R. 2021. Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sensing 13: 2486. DOI: https://doi.org/10.3390/rs1313....
60.
Park Y., Jin S., Noda I., Jung Y.M. 2020. Emerging developments in two-dimensional correlation spectroscopy (2D-COS). Journal of Molecular Structure 1217: 128405. DOI: https://doi.org/10.1016/j.mols....
61.
Prado Ribeiro L., Klock A.L.S., Filho J.A.W., Tramontin M.A., Trapp M.A., Nansen C.2018. Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory. Plant Methods 14: 54. DOI: https://doi.org/10.1186/s13007....
62.
Rehman T.U., Mahmud M.S., Chang Y.K., Jin J., Shin J. 2019. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture 156: 585–605. DOI: https://doi.org/10.1016/j.comp....
63.
Roldán J.J., del Cerro J., Garzón-Ramos D., Garcia-Aunon P., Garzón M., de León J., Barrientos A. 2018. Robots in agriculture: state of art and practical experiences. p. 67–90. In: “Service Robots”. IntechOpen. DOI: http://doi.org/10.5772/intecho....
64.
Sabrol H., Kumar S. 2015. Recent studies of image and soft computing techniques for plant disease recognition and classification. International Journal of Computer Applications 126 (1): 44–55. DOI: http://dx.doi.org/10.5120/ijca....
65.
Saddik A., Latif R., el Ouardi A., Elhoseny M., Khelifi A. 2022. Computer development based embedded systems in precision agriculture: tools and application. Acta Agriculturae Scandinavica, Section B – Soil & Plant Science 72 (1): 589–611. DOI: https://doi.org/10.1080/090647....
66.
Sankaran S., Mishra A., Ehsani R., Davis C. 2010. A review of advanced techniques for detecting plant diseases. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72 (1): 1–13. DOI: https://doi.org/10.1016/j.comp....
67.
Shakti M. 2021. Emerging technologies – principles and applications in precision agriculture. p. 31–54. In: “Data Science in Agriculture and Natural Resource Management” (Reddy G.P.O., Raval M.S., Adinarayana J., Chaudhary S., eds.). Springer, Singapore. DOI: https://doi.org/10.1007/978-98....
68.
Shibusawa S. 2001. Precision farming approaches to small farm agriculture. IFAC Proceedings Volumes 34 (2): 22–27. DOI: https://doi.org/10.1016/S1474-....
69.
Supriya J., Gopal R. 2021. IoT-enabled smart farming: challenges and opportunities. p.123–139. In: “Smart Agriculture Automation Using Advanced Technologies” (Choudhury A., Biswas A., Singh T.P., Ghosh S.K., eds.). Springer, Singapore. DOI: https://doi.org/10.1007/978-98....
70.
Susovan J., Ranjan P., Bijan S. 2021. Detection of rotten fruits and vegetables using deep learning. p. 31–41. In: “Computer Vision and Machine Learning in Agriculture” (Uddin M.S., Bansal J.C., eds.). Springer, Singapore. DOI: http://dx.doi.org/10.1007/978-....
71.
Tucker C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8 (2): 127–150. DOI: https://doi.org/10.1016/0034-4....
72.
van Klink, R., August, T., Bas, Y., Bodesheim, P., Bonn, A., Fossøy, F., Høye, T.T., Jongejans, E., Menz, M.H.M., Miraldo, A., Roslin, T., Roy, H.E., Ruczyński, I., Schigel, D., Schäffler, L., Sheard, J.K., Svenningsen, C., Tschan, G.F., Wäldchen, J., Zizka, V.M.A., Åström, J., Bowler, D.E. 2022. Emerging technologies revolutionise insect ecology and monitoring. Trends in Ecology & Evolution 37 (10): 872–885. DOI: https://doi.org/10.1016/j.tree....
73.
Vishnoi V.K., Kumar K., Kumar B. 2021. Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection 128 (1): 19–53. DOI: https://doi.org/10.1007/s41348....
74.
Walter A., Finger R., Huber R., Buchmann N. 2017. Smart farming is key to developing sustainable agriculture. PNAS 114 (24): 6148–6150. DOI: https://doi.org/10.1073/pnas.1....
75.
Wu X., Guo J., Han M., Chen G. 2018. An overview of arable land use for the world economy: from source to sink via the global supply chain. Land Use Policy 76: 201–214. DOI: https://doi.org/10.1016/j.land....
76.
Yang C., Everitt J.H., Fernande C.J. 2010. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Engineering 107: 131–139. DOI: https://doi.org/10.1016/j.bios....
77.
Yuan L., Zhang J., Shi Y., Nie C., Wei L., Wang J. 2014. Damage mapping of powdery mildew in winter wheat with high-resolution satellite image. Remote Sensing 6: 3611–3623. DOI: https://doi.org/10.3390/rs6053....
78.
Zhang J., Huang Y., Pu R., Gonzalez-Moreno P., Yuan L., Wu K., Huang W. 2019. Monitoring plant diseases and pests through remote sensing technology: a review. Computers and Electronics in Agriculture 165: 104943. DOI: https://doi.org/10.1016/j.comp....
79.
Zhang J., Huang Y., Yuan L., Yang G., Chen L., Zhao C. 2015. Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale. Pest Management Science 72: 335–348. DOI: https://doi.org/10.1002/ps.400....
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